{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T03:12:56Z","timestamp":1774062776110,"version":"3.50.1"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":78,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100017634","name":"LG Electronics","doi-asserted-by":"publisher","award":["C2024029824"],"award-info":[{"award-number":["C2024029824"]}],"id":[{"id":"10.13039\/501100017634","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["RS-2023-00213633"],"award-info":[{"award-number":["RS-2023-00213633"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008783","name":"National Research Council of Science and Technology","doi-asserted-by":"publisher","award":["GTL25021-230"],"award-info":[{"award-number":["GTL25021-230"]}],"id":[{"id":"10.13039\/501100008783","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Fingerprint resistance in consumer products is gaining prominence, necessitating reliable anti\u2010fingerprint quantification methods. Traditional approaches include contact angle measurements and image\u2010based methods. However, these often fail under realistic conditions due to transparency and complex textures, limiting accurate fingerprint resistance quantification. Herein, we propose an AI\u2010based framework inspired by human vision that distinguishes fingerprints from complex backgrounds and ranks their visibility from a single photographic image. AW\u2010Net, a novel segmentation tool, achieves 99% cluster accuracy and 93% pixel accuracy on glass surfaces. We introduce a grayscale fingerprint grading metric with five ordinal visibility levels, enabling interpretable, scalable fingerprint resistance quantification closely aligned with human visual perception. The framework establishes its universal applicability for fingerprint resistance quantification across diverse material classes, with 97% cluster accuracy and 90% pixel accuracy on complex and matte wood surfaces, underscoring that its high performance extends beyond relatively simple and glossy glass to more challenging substrates. Overall, by integrating composite visual cues through data augmentation and structured modeling, the framework delivers robust and reliable fingerprint resistance quantification, resolving visibility into five distinct levels across consumer product surfaces.<\/jats:p>","DOI":"10.1155\/int\/3069561","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T02:07:13Z","timestamp":1774058833000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Framework for Universal Quantification of Surficial Anti\u2010Fingerprint Performance"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2143-1365","authenticated-orcid":false,"given":"Byunghwa","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6557-8751","authenticated-orcid":false,"given":"Junho","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0939-1696","authenticated-orcid":false,"given":"Sangwook","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2024.110180"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apsusc.2010.10.101"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.porgcoat.2017.07.003"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2021.132902"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.2147\/rrfms.s94192"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2023.107622"},{"key":"e_1_2_12_7_2","doi-asserted-by":"crossref","DOI":"10.1038\/nature14539","article-title":"Deep Learning","author":"LeCun Y.","year":"2015","journal-title":"Nature"},{"key":"e_1_2_12_8_2","doi-asserted-by":"crossref","unstructured":"HeK.et al. Deep Residual Learning for Image Recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Denver.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_12_9_2","doi-asserted-by":"crossref","unstructured":"RonnebergerO.et al. U-Net: Convolutional Networks for Biomedical Image Segmentation Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015 Munich Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"RedmonJ.et al. You Only Look Once: Unified Real-Time Object Detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Las Vegas.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_12_11_2","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"Simonyan K.","year":"2014"},{"key":"e_1_2_12_12_2","volume-title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays With Deep Learning","author":"Rajpurkar P.","year":"2017"},{"key":"e_1_2_12_13_2","doi-asserted-by":"crossref","unstructured":"XieS.et al. Aggregated Residual Transformations for Deep Neural Networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 Honolulu.","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_2_12_14_2","unstructured":"YuF.andKoltunV. Multi-Scale Context Aggregation by Dilated Convolutions International Conference on Learning Representations (ICLR) 2016 San Juan."},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu080"},{"key":"e_1_2_12_16_2","unstructured":"Cell Tracking Challenge Web Page of the Cell Tracking Challenge http:\/\/www.codesolorzano.com\/celltrackingchallenge\/CellTrackingChallenge\/Welcome.html."},{"key":"e_1_2_12_17_2","article-title":"Fabric Defect Detection Method Based on Improved U-Net","author":"Liu R.","year":"2021","journal-title":"Journal of Physics: Conference Series"},{"key":"e_1_2_12_18_2","volume-title":"ResNet in ResNet: Generalizing Residual Architectures","author":"Targ S.","year":"2016"},{"key":"e_1_2_12_19_2","unstructured":"TanM.andLeQ. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks International Conference on Machine Learning (ICML) 2019 Long Beach."},{"key":"e_1_2_12_20_2","volume-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","author":"Howard A. G.","year":"2017"},{"key":"e_1_2_12_21_2","volume-title":"The Effectiveness of Data Augmentation in Image Classification Using Deep Learning","author":"Perez L.","year":"2017"},{"key":"e_1_2_12_22_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_12_23_2","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek\u00d6.et al. 3D U-Net: Learning Dense Volumetric Segmentation From Sparse Annotation Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2016 Athens Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_2_12_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_2_12_25_2","article-title":"Unsupervised Domain Adaptation in Brain Lesion Segmentation With Adversarial Networks","author":"Kamnitsas K.","year":"2017","journal-title":"Information Processing in Medical Imaging (IPMI)"},{"key":"e_1_2_12_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2973595"},{"key":"e_1_2_12_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-010-9221-z"},{"key":"e_1_2_12_28_2","doi-asserted-by":"crossref","DOI":"10.1039\/D1AN02293H","article-title":"Monitoring the Chemical Changes in Fingermark Residue Over Time Using Synchrotron Infrared Spectroscopy","volume":"147","author":"Boseley","year":"2022","journal-title":"Analyst"},{"key":"e_1_2_12_29_2","volume-title":"Nano-Topographical Changes in Latent Fingerprint due to Degradation Over Time Studied by Atomic Force Microscopy","author":"Svato\u0148ov\u00e1","year":"2024"},{"key":"e_1_2_12_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24175652"},{"key":"e_1_2_12_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10721-6"},{"key":"e_1_2_12_32_2","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","author":"Zhou Z.","year":"2018","journal-title":"Deep Learning in Medical Image Analysis (DLMIA)"},{"key":"e_1_2_12_33_2","volume-title":"Attention U-Net: Learning Where to Look for the Pancreas","author":"Oktay O.","year":"2018"},{"key":"e_1_2_12_34_2","unstructured":"JhaD.et al. Double U-Net: A Deep Convolutional Neural Network for Medical Image Segmentation IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2020 Barcelona Spain."},{"key":"e_1_2_12_35_2","unstructured":"AmolS. J. I.et al. Synthetic Latent Fingerprint Generation Using Style Transfer 2023 International Conference of the Biometrics Special Interest Group (BIOSIG) 2023 Darmstadt Germany IEEE."},{"key":"e_1_2_12_36_2","volume-title":"Studies in Optics","author":"Michelson A. A.","year":"1995"},{"key":"e_1_2_12_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.visres.2013.04.015"},{"key":"e_1_2_12_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_12_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"e_1_2_12_40_2","volume-title":"Adam: A Method for Stochastic Optimization","author":"Kingma D. P.","year":"2014"},{"key":"e_1_2_12_41_2","doi-asserted-by":"crossref","unstructured":"ShehuY. I. Ruiz-GarciaA. PaladeV. andJamesA. Detection of Fingerprint Alterations Using Deep Convolutional Neural Networks Proceedings of the International Conference on Artificial Neural Networks (ICANN 2018) 2018 Rhodes Greece Springer-Verlag https:\/\/www.kaggle.com\/datasets\/ruizgara\/socofing.","DOI":"10.1007\/978-3-030-01418-6_6"},{"key":"e_1_2_12_42_2","unstructured":"AmbientCG Public Domain Texture Repository https:\/\/ambientcg.com\/."},{"key":"e_1_2_12_43_2","unstructured":"WyzykowskiA. B. V.et al. IEEE Biometrics Council 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 Waikoloa."},{"key":"e_1_2_12_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2018.2878773"},{"key":"e_1_2_12_45_2","doi-asserted-by":"crossref","unstructured":"L\u00e9blM. \u0160roubekF. andFlusserJ. Impact of Image Blur on Classification and Augmentation of Deep Convolutional Networks Scandinavian Conference on Image Analysis 2023 Lapland Finland.","DOI":"10.1007\/978-3-031-31438-4_8"},{"key":"e_1_2_12_46_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0582-9"},{"key":"e_1_2_12_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2019.2947506"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/3069561","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/3069561","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/3069561","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T02:07:24Z","timestamp":1774058844000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/3069561"}},"subtitle":[],"editor":[{"given":"Richard","family":"Murray","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/int\/3069561"],"URL":"https:\/\/doi.org\/10.1155\/int\/3069561","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-09-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3069561"}}